An important aspect of technology and research related to sensory neurophysiology is the detection and analysis of neural signals. Neural signals are electrical signals from neurons in a neural network. With current technology, recordings have been made of electrical signals from neurons in neural networks such as cell cultures, hippocampal slices, in vivo tissue, and even the human brain. For example, to acquire signals from different neurons in a region of the brain, one or more electrode sensors can be positioned in a subject's brain for recording action potential arrival, times in neurons or nerve cells resulting from spontaneous or stimulus-evoked activity.
Of particular importance in many neurophysiology applications is the analysis of spike trains, which reflect the “firing” of neurons. A spike in this context can be broadly defined as a sharp transient that is visibly different from background noise. A specific application is the brain-machine interface, which must extract information from neural recordings collected in the motor cortex of a brain with the intended goal of creating predictive models for hand movement or direct control of a robotic device.
Currently available instruments and surgical procedures allow for such recordings from hundreds of electrodes at once. However, a yet unresolved problem is how to mitigate the resulting bottleneck that occurs when transfer is attempted of large bandwidth data streams, such as each channel being sampled at 25 kHz, 16 bits, without requiring that a subject be tethered with wires extending from one or more electrodes and connecting to a signal processing unit.
Conventional as well as more-recently proposed spike detection techniques typically suffer from shortcomings that preclude their use in low-power, stand-alone devices of sufficiently small size for implanting in a subject. Amplitude thresholding, for example, usually does not perform adequately since the signal-to-noise ratio (SNR) during detection typically drops. The technique also has been shown generally to lack robustness to DC shifts.
Both template matching and matched filtering, although sometimes providing reasonably accurate spike detection, typically require intensive computations and stable templates. These conventional techniques typically require hardware implementations that are not easily reduced to a size that is feasible for implantation in a subject. Similarly, a traditional wavelet technique, though performing well for real-time analyses, typically consumes too much power to be rendered in an implantable device.